Ultrasound image enhancement using improved Fixed Point Independent Component Analysis

Medical ultrasound imaging technique uses pulsed acoustic waves that are transmitted and received by a hand-held transducer. This is a mature technology that is widely used around the world. The advantages of ultrasound images are that it is cost-effective, flexible, and does not require ionizing radiation. However, the image quality is affected by degradation of ultrasound images when propagating through biological tissues. Many authors have proposed different methods like Independent component analysis (ICA) based approaches to enhance the quality of image by reducing the mixing effect within the image, by maximizing the non-gaussianity in ultrasound data,. A novel method Fixed Point Independent Component Analysis (FPICA) is proposed in this paper. This method has been used for enhancing the analyzing quality of ultrasound image by using non-gaussian distribution. Due to the presence of neural algorithms, it is quite robust, computationally simple and very fast convergence, in regard of the spectral distributions of ultrasound images. Hence, this proposed FPICA approach plays a major role in enhancement of medical ultrasound image.